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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该实现包含动态可视化\n",
    "调用逻辑\n",
    "- train(net, train_iter, test_iter, loss, num_epoch,optimizer)\n",
    "内部调用动态可视化 \n",
    "animator = d2l.Animator(xlabel=, xlim=[], ylim=[], legend=)\n",
    "调用train_epoch作为一轮的实际训练，返回训练损失；  训练精度不应在此时计算，要开启评估模式关闭dropout\n",
    "    - net 包含定义与参数初始化\n",
    "    - train_iter 可以使用 d2l.load_array((train_feature, train_label), batch_size) 实现，数据迭代器解引用能得到对应的特征与标签\n",
    "    - loss = nn.BCEWithLogitsLoss() 参数默认 mean batch平均 此外还是sum 和 none\n",
    "    - optimizer = torch.optim.SDG(net.parameters(), lr)\n",
    "    - train_epoch(net, train_iter, loss, optimizer) loss使用默认平均值，内部使用Accumulator计算损失总和，因为最后一批可能不足量\n",
    "    - accuracy(net, iter), 要设置评估模式\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "from torch import nn\n",
    "from d2l import torch as d2l\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载清洗后的数据\n",
    "train_feature = pd.read_csv('../data/processed/train_feature.csv')\n",
    "train_label = pd.read_csv('../data/processed/train_label.csv')\n",
    "\n",
    "# 测试集没有label的，将训练集划分一块作为验证集\n",
    "test_feature = pd.read_csv('../data/processed/test_feature.csv')\n",
    "\n",
    "# bool 类型数据要转为int 才可以变为tensor\n",
    "bool_cols = train_feature.dtypes[train_feature.dtypes == 'bool'].index\n",
    "train_feature[bool_cols] = train_feature[bool_cols].astype(int)\n",
    "test_feature[bool_cols] = test_feature[bool_cols].astype(int)\n",
    "\n",
    "train_feature = torch.tensor(train_feature.values, dtype=torch.float32)\n",
    "train_label = torch.tensor(train_label.values, dtype=torch.float32)\n",
    "test_feature = torch.tensor(test_feature.values, dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义主要参数\n",
    "feature_num = train_feature.shape[1]\n",
    "hidden_num = 256\n",
    "output_num = 1\n",
    "init_weight_std = 0.01\n",
    "\n",
    "train_set_ratio = 0.7\n",
    "batch_size = 64\n",
    "\n",
    "dropout = 0.2\n",
    "learn_rate = 0.02\n",
    "weight_decay = 0\n",
    "num_epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义网络 及 参数初始化\n",
    "def init_weights(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.normal_(m.weight, std=init_weight_std)\n",
    "\n",
    "def get_net(dropout1):\n",
    "    net = nn.Sequential(nn.Linear(feature_num, hidden_num),\n",
    "                        nn.ReLU(),\n",
    "                        nn.Dropout(dropout1),\n",
    "                        nn.Linear(hidden_num, output_num))\n",
    "    \n",
    "    net.apply(init_weights)\n",
    "    return net\n",
    "\n",
    "net = get_net(dropout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 制作迭代器，不包含k折交叉\n",
    "# TODO : 2025/01/15 划分时应打乱划分 li hl\n",
    "n_train = int(train_label.shape[0] * train_set_ratio)\n",
    "\n",
    "train_iter = d2l.load_array((train_feature[:n_train, :], train_label[:n_train, :]), batch_size)\n",
    "test_iter = d2l.load_array((train_feature[n_train:, :], train_label[n_train:, :]), batch_size)\n",
    "train_data = train_feature[:n_train, :]\n",
    "train_value = train_label[:n_train, :]\n",
    "test_data = train_feature[ n_train:, :]\n",
    "valid_value = train_label[n_train:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义损失函数， 默认为小批量平均， BCEWithLogitsLoss 带有logistics 将数值转化为概率\n",
    "loss = nn.BCEWithLogitsLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义优化器\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=learn_rate, weight_decay=weight_decay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义 train_epoch\n",
    "def train_epoch_ch3(net, train_iter, loss, optimizer):\n",
    "    # 训练模型一个迭代周期, 将模型设置为训练模式\n",
    "    net.train()\n",
    "    # 训练损失总和、样本数\n",
    "    metric = d2l.Accumulator(2)\n",
    "\n",
    "    for X, y in train_iter:\n",
    "        # 计算梯度并更新参数\n",
    "        y_hat = net(X)\n",
    "        l = loss(y_hat, y)\n",
    "        optimizer.zero_grad()\n",
    "        l.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # numel 返回元素数量， l计算的是小批量损失均值，为避免最后一批数量不足的影响，使用累加器\n",
    "        metric.add(float(l.item()*y.numel()), y.numel())\n",
    "    \n",
    "    average_loss = metric[0] / metric[1]\n",
    "    return average_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_accuracy(net, iter):\n",
    "    # TODO 直接使用全部数据不用迭代器\n",
    "    net.eval()\n",
    "\n",
    "    metric = d2l.Accumulator(2)\n",
    "    for X, y in iter:\n",
    "        probality = torch.sigmoid(net(X))\n",
    "        prediction = (probality>=0.5).long()\n",
    "        correct_predition = (prediction == y).sum().item()\n",
    "\n",
    "        metric.add(correct_predition, y.numel())\n",
    "\n",
    "    accuracy = 1.0 * metric[0] / metric[1]\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(net, train_iter, test_iter, loss, num_epochs, optimizer):\n",
    "    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1],\n",
    "                        legend=['train loss', 'train acc', 'test acc'])\n",
    "    for epoch in range(num_epochs):\n",
    "        train_loss = train_epoch_ch3(net, train_iter, loss, optimizer)\n",
    "        train_acc = get_accuracy(net, train_iter)\n",
    "        test_acc = get_accuracy(net, test_iter)\n",
    "\n",
    "        animator.add(epoch + 1, (train_loss, train_acc, test_acc))\n",
    "    # TODO : 添加 loss accuracy 的记录与保存  250115 li hl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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